Identifier notes

Handling alternative identifiers in linked data involves ensuring that each identifier is properly linked to the same resource and that they are easily resolvable within your system. Here's a general approach to manage this:

1. Use HTTP URIs for Your Main Identifiers: Ensure that your main identifiers are HTTP URIs, which are easily resolvable over the web and can be dereferenced to obtain more information about the resource.

2. Linking URIs with Short Identifiers:

   • You can use RDF (Resource Description Framework) to express these links. For example, you might have a URI http://example.org/item123 and a short identifier ABC123. You can link them using RDF like this:

     <rdf:Description rdf:about="http://example.org/item123">
       <skos:notation>ABC123</skos:notation>
     </rdf:Description>

   • The skos:notation property is used here to associate the short identifier with the URI.

3. SPARQL for Lookups:

   • Implement SPARQL queries in your code to perform lookups using either the URI or the short identifier. SPARQL is a powerful query language for RDF data that can handle such queries efficiently.

4. Maintain a Mapping Table:

   • Maintain a table or a document that maps URIs to their corresponding short identifiers and vice versa. This can be part of your linked data platform or a separate service that your application can query.

5. Use Content Negotiation:

   • Implement content negotiation in your web server to return the correct resource representation based on the identifier used. This means that whether a user accesses the resource using the URI or the short identifier, they will receive the appropriate data.

6. Documentation:

   • Document the usage of both types of identifiers in your system's documentation so that users and developers understand how to use them for lookups.

7. Persistent Identifier Systems:

   • Consider using a persistent identifier system like DOI or Handle, which can manage multiple identifiers for the same resource and ensure their persistence over time.

For more detailed guidance, you can refer to resources like the International Journal of Epidemiology for evaluating linkage quality and the FREYA project for examples of linking across identifiers. Additionally, understanding the principles of Linked Data can provide a solid foundation for managing identifiers in a web-based environment. Remember to keep your identifiers consistent and well-documented to facilitate easy lookup and resolution in your code..

Source: Conversation with Bing, 4/11/2024 (1) guide to evaluating linkage quality for the analysis of linked data .... https://academic.oup.com/ije/article/46/5/1699/4107250. (2) Case study: Linking across identifiers. https://project-freya.readme.io/docs/examples-of-linking-across-identifiers. (3) Introduction to Linked Data - Cambridge Semantics. https://cambridgesemantics.com/blog/semantic-university/intro-semantic-web/intro-linked-data/. (4) undefined. https://doi.org/10.1093/ije/dyx177. (5) en.wikipedia.org. https://en.wikipedia.org/wiki/Linked_data.

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In linked data representations, @id in JSON-LD and dcterms:identifier serve similar purposes but are not exactly the same.

The @id in JSON-LD is a unique identifier that represents a specific resource or node within the document. It's used to uniquely identify things within a JSON-LD context and can be a URL or a URI fragment¹. This identifier helps link different pieces of related information across the web.

On the other hand, dcterms:identifier is a property from the Dublin Core Metadata Terms (DCMI Metadata Terms) used to provide a unique identifier for a resource. It's part of the Dublin Core standard, which is often used in RDF and other metadata schemas to describe digital resources.

While both are used to uniquely identify resources, they are part of different standards and specifications. @id is specific to JSON-LD and is used within its context to link data, whereas dcterms:identifier is a broader term used across various metadata standards, including RDF, to denote a unique identifier for a resource.

In practice, you could have a JSON-LD representation of a resource where @id is used to define its identifier within the JSON-LD context, and dcterms:identifier could also be included to provide a unique identifier that aligns with the Dublin Core standard. They can coexist in a linked data representation, serving their respective purposes within their standards.

Source: Conversation with Bing, 4/11/2024 (1) uri - @id vs. URL for linking JSON-LD nodes - Stack Overflow. https://stackoverflow.com/questions/41071235/id-vs-url-for-linking-json-ld-nodes. (2) What does mean @id and @type in json-ld context?. https://stackoverflow.com/questions/64570249/what-does-mean-id-and-type-in-json-ld-context. (3) What is the difference between json-ld @id and @type in the @context .... https://stackoverflow.com/questions/24318237/what-is-the-difference-between-json-ld-id-and-type-in-the-context. (4) schema.org - Can JSON-LD "@id" be used to link to an item described on .... https://stackoverflow.com/questions/58060499/can-json-ld-id-be-used-to-link-to-an-item-described-on-another-web-page. (5) undefined. http://schema.org. (6) undefined. http://www.example.com/. (7) undefined. http://www.example.com/news. (8) undefined. http://www.example.com/%29.

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And, yes, a resource can have more than one dcterms:identifier property. The Dublin Core Metadata Initiative (DCMI) guidelines do not restrict a resource to a single identifier. Multiple dcterms:identifier properties can be used to accommodate different systems or contexts where a resource might be identified in various ways. For example, a book might have an ISBN, a DOI, and a library call number, all of which could be represented as separate dcterms:identifier properties within the same metadata record¹.

This flexibility allows for a more comprehensive description and easier integration with different systems and standards, enhancing the interoperability of the metadata.

Source: Conversation with Bing, 4/11/2024 (1) DCMI: Publishing Metadata - Dublin Core. https://www.dublincore.org/resources/userguide/publishing_metadata/. (2) DCMI: DCMI Metadata Terms - Dublin Core. https://www.dublincore.org/specifications/dublin-core/dcmi-terms/. (3) DCMI: Guidelines for implementing Dublin Core™ in XML. https://www.dublincore.org/specifications/dublin-core/dc-xml-guidelines/. (4) undefined. http://www.example.com/. (5) undefined. http://purl.org/dc/elements/1.1/. (6) undefined. http://purl.org/dc/terms/. (7) undefined. http://purl.org/dc/dcmitype/. (8) undefined. http://dublincore.org/specifications/dublin-core/dcmi-terms/2020-01-20/. (9) undefined. https://www.dublincore.org/specifications/dublin-core/dcmi-terms/release_history/.

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Certainly! To estimate the standard deviation from the mean, 75th percentile (Q3), and 90th percentile, we can use the properties of a normal distribution. However, please note that this estimation assumes the data follows a normal distribution, which may not always be the case.

Here's a Python function that uses the z-scores corresponding to the 75th and 90th percentiles to estimate the standard deviation:

from scipy.stats import norm

def estimate_std(mean, percentile_75, percentile_90):
    # Z-scores for 75th and 90th percentile in a standard normal distribution
    z_75 = norm.ppf(0.75)
    z_90 = norm.ppf(0.90)

    # Calculate the standard deviation using the z-scores and percentile values
    std_estimated = (percentile_90 - mean) / z_90

    # Alternatively, you could use the 75th percentile to estimate
    # std_estimated_alt = (percentile_75 - mean) / z_75

    return std_estimated

# Example usage:
mean = 50  # replace with your mean value
percentile_75 = 60  # replace with your 75th percentile value
percentile_90 = 70  # replace with your 90th percentile value

estimated_std = estimate_std(mean, percentile_75, percentile_90)
print(f"Estimated Standard Deviation: {estimated_std}")

Remember to replace mean, percentile_75, and percentile_90 with your actual values. This function will give you an estimated standard deviation based on the provided percentiles. Keep in mind that the accuracy of this estimation heavily relies on the assumption that the data is normally distributed. If the data distribution is significantly different, the estimation may not be accurate.

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